The Ultra Wide Band (UWB) technology, which may be used for the physical layer for a low-power short distance (up to 10 m) radio transmission technique for high data rates, may for instance be implemented by a multicarrier modulation. Said multicarrier modulation may be represented by an Orthogonal Frequency Division Multiplexing (OFDM) modulation, a Multi Band (MB) OFDM modulation or any other multicarrier modulation.
However, said multicarrier UWB system often has to coexist with at least one narrowband radio service that operates within the spectrum of said UWB system. In general, said narrowband radio system has any bandwith that is smaller than the UWB system's bandwith. Thus, said narrowband system looks like an interferer for at least one of the carriers of said multicarrier UWB system, and, more problematically, an active UWB system looks like an increased noise level for said narrowband transmission system. To minimize this interference affecting said narrowband radio service, the UWB system's transmitting power in the frequency spectrum of said narrowband radio service has to be limited.
Most notably, the Active Interference Cancellation (AIC) Technique, presented by Hirohisa Yamaguchi on the 34th European Microwave Week Conference, which has been held in Amsterdam, Netherlands on 11-15 Oct. 2004, has been introduced to mitigate the level of interference produced by UWB MB-OFDM radio systems to narrowband systems. Said AIC Technique is published in the conference proceedings of said 34th European Microwave Week under the title “Active Interference Cancellation Technique for MB-OFDM Cognitive Radio”, on pages 1105-1108. At first, the carriers of said MB-OFDM system that fall within the narrowband reception band, in the following called victim receiver's band, are nulled out. In OFDM and also in MB-OFDM, any carriers are placed at a regular frequency interval to avoid inter-carrier interference, but due to the properties of sinc-function, which characterizes the spectrum of an OFDM carrier, the inter-carrier interference between the carrier-frequencies becomes large. Thus, nulling the carriers that fall within the victim receiver's band does not normally lead to a sufficient limitation of transmit power in said victim receiver's band, as the remaining active carriers introduce interference concerning the spectrum in between the carrier frequencies of said nulled carriers. Particularly, most of said interference is determined by the tones of the active carriers neighbored to said nulled carriers. Hence, said AIC approach proposes to calculate the two tones associated to the carriers located on each side of the victim receiver's band in order to minimize the interference inside the victim receiver's band. Said two tones are denoted as AIC tones in the following. In order to calculate the AIC tones, the carriers that fall within the victim receiver's band are nulled and the carriers corresponding to the AIC tones are also nulled. Afterwards, the 128-point IFFT of the signal, which is appropriately defined by data carriers, pilot carriers, guard carriers and said nulled carriers, is calculated to obtain the corresponding frequency spectrum. In order to evaluate the amount of interference that is still present over the victim receiver's bandwith, the 128K-IFFT with an upsampling factor K is applied to said signal and the MB-OFDM spectrum is interpolated. Then, the total MB-OFDM interference power that exists over each frequency point within the victim receiver's band is calculated, and the two optimal AIC coefficients are obtained by applying the minimum mean squared error approach in order to minimize the total MB-OFDM interference that exists over the victim receiver's band. The solution of said minimum mean squared error optimization problem requires a matrix inversion. Afterwards, the quantized AIC coefficient values are assigned to said AIC tones that are associated with the carriers placed on each side of the victim receiver's band. Finally, the MB-OFDM signal, including the AIC tones, is transmitted.
However, the AIC approach leads to high complexity, as it requires interpolation of the frequency domain signal and calculation of a matrix inverse in order to solve the minimum mean squared error optimization problem. Moreover, the quantization of the AIC coefficients limits algorithm performance.